Report for answering 2nd Part of Capstone Project (Full Report)¶

Capstone Project - Week 2 (Full Report)¶

Table of Contents¶

Introduction: Business Problem
Data
Methodology
Results
Discussion
Conclusion

Introduction: Business Problem ¶

This project attempts to examine a city (NYC) to find an optimal neighborhood for opening a mexican restaurant.

New York City(pre-COVID) is a city bustling with restaurants and pedestrian traffic. The code will examine data to find a location that is based on the following criteria:

A new restaurant prefers to be opened near other restaurants. (Restaurants prefer to be clustered together rather than isolated)
Restaurants prefer to be near a strong customer "draw" (E.g. a School, Office District, major venue: stadium, theatre)
Restaurants prefer to be the only restuarant of its type in neighborhood (e.g. A mexican restaurant would not want to be near another mexican restaurant.)

Data ¶

This where you will type up your discussion of the data e en before you get into the code!! Based on the criteria discussed in the introduction, the data collected would be a datasets consisting of all venues in the NYC area, broken down into smaller manageable datasets.

The data will be collected using the FourSquare API. All coordinates would be looked up and pandas would be created based on data returned by API. Additionally, maps and other visualization tools would be utilize to assit in making a decision on the best locations for opening a Mexican Restaurant. (All the code for this project is located after this report)

There would be an initial grab of all the venues and this would be filtered out into relevant subsets.

Methodology ¶

The newyork_data.json file is used as a starting source for this project instead of the google maps API. This json data is then loaded into a dataframe. Initially, it contains information on all the neighborhoods in all the NY boroughs. This data is filtered down to focus only on Manhattan neighborhoods (with their respective coordinates.

A pre-written function is used to iterate through the filtered manhattan_data dataset whilst making calls to the FourSquare API to generate a new dataset (manhattan_venues) which contains all the venues located in the FourSquare dataset for Manhattan.

This data is the manipulated and cleaned to produce subsets of data that includes only restaurant as the category and also more specifically only MEXICAN restaurants. These datasets would then serve as our primary data source for visualizing and making decisions about possible good locations to open a Mexican Restaurant in Manhattan.

Results ¶

The following images are generated as part of the visualization of data collected.

Image showing Restaurants in Manhattan (Map 1)¶

Blue dots are Mexican Restaurants. Red Dots are non-Mexican Restaurants

The following image shows the concentration of Mexican restaurants. (Map 2)¶

The following image shows the KMeans clustering data. (Map 3)¶

Discussion ¶

Here is your discussion Cell. Please make the most of it. Based on preliminary data, we can see that an ideal location for opening a Mexican Restaurant may be in the areas where they are notably absent, specifically, the lower West Side and West Mid-town (See Map 2).

Conclusion ¶

Here is your conclusion CELL for discussion

-------------------- Code Section --------------------¶

This Notebook will use data to suggest the best location for opening a Mexican restaurant in NY city¶

------This is a completely fictional scenario ------

--------It assumes the following--------

     --------Restaurants (owners) prefer to be located near to other restaurants --------
     --------Restaurants (owners) prefer to be near a potential market (School, Office District)
     --------Restaurants (owners) prefer not to be near immediate competitors (e.g. A Mexican restaurant does not
             want to be located near another Mexican restaurant, but would prefer being close to Italian, 
             Ethopian etc.)

pip install geopy

Collecting geopy
  Downloading https://files.pythonhosted.org/packages/07/e1/9c72de674d5c2b8fcb0738a5ceeb5424941fefa080bfe4e240d0bacb5a38/geopy-2.0.0-py3-none-any.whl (111kB)
     |████████████████████████████████| 112kB 24.2MB/s eta 0:00:01
Collecting geographiclib<2,>=1.49 (from geopy)
  Downloading https://files.pythonhosted.org/packages/8b/62/26ec95a98ba64299163199e95ad1b0e34ad3f4e176e221c40245f211e425/geographiclib-1.50-py3-none-any.whl
Installing collected packages: geographiclib, geopy
Successfully installed geographiclib-1.50 geopy-2.0.0
Note: you may need to restart the kernel to use updated packages.

#Import all required libraries
import numpy as np # library to handle data in a vectorized manner

import pandas as pd # library for data analsysis
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

import json # library to handle JSON files

#!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab
from geopy.geocoders import Nominatim # convert an address into latitude and longitude values

import requests # library to handle requests
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe

# Matplotlib and associated plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors

# import k-means from clustering stage
from sklearn.cluster import KMeans

#!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab
import folium # map rendering library

with open('newyork_data.json') as json_data:
    newyork_data = json.load(json_data)
#newyork_data

neighborhoods_data = newyork_data['features']
# define the dataframe columns
column_names = ['Borough', 'Neighborhood', 'Latitude', 'Longitude'] 

# instantiate the dataframe
neighborhoods = pd.DataFrame(columns=column_names)

for data in neighborhoods_data:
    borough = neighborhood_name = data['properties']['borough'] 
    neighborhood_name = data['properties']['name']
        
    neighborhood_latlon = data['geometry']['coordinates']
    neighborhood_lat = neighborhood_latlon[1]
    neighborhood_lon = neighborhood_latlon[0]
    
    neighborhoods = neighborhoods.append({'Borough': borough,
                                          'Neighborhood': neighborhood_name,
                                          'Latitude': neighborhood_lat,
                                          'Longitude': neighborhood_lon}, ignore_index=True)

neighborhoods.head()

#Focus is on Manhattan for potential new restaurant...
manhattan_data = neighborhoods[neighborhoods['Borough'] == 'Manhattan'].reset_index(drop=True)
manhattan_data.head()

Use data pulled from FourSquare to locate as many restaurant venues as possible while paying attention to the locations of MEXICAN Restaurant¶

The following cell is hidden but it contains foursquare credentials

# @hidden_cell
CLIENT_ID = 'V4T0HDCPWZVSPMMHVTNJAT3QORC2H10MKVJEM44ZRDTY2XKW' # your Foursquare ID
CLIENT_SECRET = 'KXYRVUGUUAMPJTPGNSQD31202ZB0ZPSQGHUKZVKWGHRK5KB2' # your Foursquare Secret
VERSION = '20180605' # Foursquare API version

LIMIT = 100
radius = 750

# This function is for getting all venues in specific coordinates
def getNearbyVenues(names, latitudes, longitudes, radius=500):
    
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
        print(name)
            
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT)
            
        # make the GET request
        results = requests.get(url).json()["response"]['groups'][0]['items']
        
        # return only relevant information for each nearby venue
        venues_list.append([(
            name, 
            lat, 
            lng, 
            v['venue']['name'], 
            v['venue']['location']['lat'], 
            v['venue']['location']['lng'],  
            v['venue']['categories'][0]['name']) for v in results])

    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
    nearby_venues.columns = ['Neighborhood', 
                  'Neighborhood Latitude', 
                  'Neighborhood Longitude', 
                  'Venue', 
                  'Venue Latitude', 
                  'Venue Longitude', 
                  'Venue Category']
    
    return(nearby_venues)

manhattan_venues = getNearbyVenues(names=manhattan_data['Neighborhood'],
                                   latitudes=manhattan_data['Latitude'],
                                   longitudes=manhattan_data['Longitude']
                                  )

Marble Hill
Chinatown
Washington Heights
Inwood
Hamilton Heights
Manhattanville
Central Harlem
East Harlem
Upper East Side
Yorkville
Lenox Hill
Roosevelt Island
Upper West Side
Lincoln Square
Clinton
Midtown
Murray Hill
Chelsea
Greenwich Village
East Village
Lower East Side
Tribeca
Little Italy
Soho
West Village
Manhattan Valley
Morningside Heights
Gramercy
Battery Park City
Financial District
Carnegie Hill
Noho
Civic Center
Midtown South
Sutton Place
Turtle Bay
Tudor City
Stuyvesant Town
Flatiron
Hudson Yards

manhattan_venues.tail(15)

Begin analysis of Data¶

##Create a Dataset of Restaurants not including Mexican Restaurants
Restaurants = manhattan_venues
Restaurants.columns = [column.replace(" ","_") for column in Restaurants]
Restaurants.rename(columns = {'Venue_Latitude':'Latitude', 'Venue_Longitude':'Longitude'}, inplace = True)
Restaurants = Restaurants.query('Venue_Category.str.contains("Restaurant")')

#Drop all Mexican Restaurants
AllRestaurants = Restaurants #(Save All Restaurants Dataset for manipulation later)
Restaurants = Restaurants[Restaurants.Venue_Category != 'Mexican Restaurant']

#Restaurants.head(10) #All non-mexican Restaurants
Restaurants.shape
#AllRestaurants.shape

(848, 7)

Generating dataset for Mexican Restaurants only. ¶

##Create a Mexican Restaurant sub-Dataset from the Manhattan_Venues Dataset and Visualize it!
mexicanRestaurants = manhattan_venues
mexicanRestaurants.columns = [column.replace(" ","_") for column in mexicanRestaurants]
mexicanRestaurants.rename(columns = {'Venue_Latitude':'Latitude', 'Venue_Longitude':'Longitude'}, inplace = True)
mexicanRestaurants = mexicanRestaurants.query('Venue_Category == "Mexican Restaurant"')

#mexicanRestaurants
mexicanRestaurants.shape
mexicanRestaurants.head()

#This data indicates that there are 55 venues fitting the Category of 'Mexican Restaurant' in NYC (Manhattan)
#Look at data on a MAP (dataset is mexicanRestaurants)
address = 'New York City, NY'

geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
#print('The geograpical coordinate of New York City are {}, {}.'.format(latitude, longitude))

map_newyork = folium.Map(location=[latitude, longitude], zoom_start=12)

# add markers to map
#Blue Dots indicate MEXICAN Restaurant 
#Red Dots indicates non-Mexican Restaurant
for lat, lng, venue, neighborhood in zip(mexicanRestaurants['Latitude'], mexicanRestaurants['Longitude'],mexicanRestaurants['Venue'], mexicanRestaurants['Neighborhood']):
    label = '{},{}'.format(venue,neighborhood)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=1,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_newyork)
        
for lat, lng, venue, neighborhood in zip(Restaurants['Latitude'], Restaurants['Longitude'], Restaurants['Venue'], Restaurants['Neighborhood']):
    label = '{},{}'.format(venue,neighborhood)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=1,
        popup=label,
        color='red',
        fill=True,
        fill_color=None,
        fill_opacity=0.7,
        parse_html=False).add_to(map_newyork)
map_newyork

Hot Encode All Restaurants dataset for analysis later down¶

##This creates a new dataset that categorizes all the venues
## one hot encoding for Categorical Data
#manhattan_onehot = pd.get_dummies(manhattan_venues[['Venue_Category']], prefix="", prefix_sep="")

## add neighborhood column back to dataframe
#manhattan_onehot['Neighborhood'] = manhattan_venues['Neighborhood'] 

## move neighborhood column to the first column
#fixed_columns = [manhattan_onehot.columns[-1]] + list(manhattan_onehot.columns[:-1])
#manhattan_onehot = manhattan_onehot[fixed_columns]

#manhattan_onehot.head()
##manhattan_onehot.shape

restaurant_onehot = pd.get_dummies(AllRestaurants[['Venue_Category']], prefix="", prefix_sep="")
restaurant_onehot['Neighborhood'] = AllRestaurants['Neighborhood']
fc = [restaurant_onehot.columns[-1]] + list(restaurant_onehot.columns[:-1])
restaurant_onehot = restaurant_onehot[fc]

restaurant_onehot.shape
restaurant_onehot.head()

Dataset below (mx_only) shows the manhattan neighborhoods that already have a mexican restaurant(s) and as a result, should be excluded from our final consideration based on initial critera.¶

# Which neighborhoods already have mexican restaurants? We do not want these neighborhoods
mx_only = restaurant_onehot[restaurant_onehot['Mexican Restaurant']>0]
mx_only = mx_only.groupby('Neighborhood').count().reset_index()

col_list = ['Neighborhood','Mexican Restaurant']
mx_only = mx_only[col_list]
mx_only.shape
mx_only.head(15)

HeatMap below shows most notably that lower westside and westside mid-town are notably absent Mexican restaurants¶

from folium.plugins import HeatMap
#basemap = generateBaseMap()

heat_map = folium.Map(location=[latitude, longitude], zoom_start=12)
m2 = mexicanRestaurants
m2 = m2[['Latitude','Longitude']]
#m2 = m2.reset_index()
m2.head()

heat_data = [[row['Latitude'],row['Longitude']] for index, row in m2.iterrows()]
HeatMap(heat_data).add_to(heat_map)
heat_map

#manhattan_grouped = manhattan_onehot.groupby('Neighborhood').mean().reset_index()
#manhattan_grouped.head()
##This dataset has the column we are keenly interested in ['Mexican Restaurant']

AllRestaurants_grouped = restaurant_onehot.groupby('Neighborhood').mean().reset_index()
AllRestaurants_grouped.head()

Because of simplicity of this endeavor, we would just pull data from neighborhoods where Mexican restaurant category is not in the top 10 venues¶

#This function sorts venues

def return_most_common_venues(row, num_top_venues):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_venues]

## This analysis may not be necessary just yet
##But Just in case it is useful! DATA is KING!

num_top_venues = 10

indicators = ['st', 'nd', 'rd']

# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_venues):
    try:
        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
    except:
        columns.append('{}th Most Common Venue'.format(ind+1))

# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns=columns)
#neighborhoods_venues_sorted['Neighborhood'] = manhattan_grouped['Neighborhood']
neighborhoods_venues_sorted['Neighborhood'] = AllRestaurants_grouped['Neighborhood']

#for ind in np.arange(manhattan_grouped.shape[0]):
#    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(manhattan_grouped.iloc[ind, :], num_top_venues)

for ind in np.arange(AllRestaurants_grouped.shape[0]):
    neighborhoods_venues_sorted.iloc[ind,1:] = return_most_common_venues(AllRestaurants_grouped.iloc[ind,:], num_top_venues)

neighborhoods_venues_sorted.shape #This sorts restaurants in each neighborhood based on their frequency
neighborhoods_venues_sorted.head()

The data above (neigborhoods_venues_sorted) shows the frequency of restaurants in neighborhoods. Ideal locations for our investigation would only include neighborhoods that do not have mexican restaurants.

Cluster the neighborhoods¶

# set number of clusters
kclusters = 7

#manhattan_grouped_clustering = manhattan_grouped.drop('Neighborhood', 1)

## run k-means clustering
#kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(manhattan_grouped_clustering)

## check cluster labels generated for each row in the dataframe
#kmeans.labels_[0:10] 

RestaurantClustering = AllRestaurants_grouped.drop('Neighborhood', 1)
kmeans = KMeans(n_clusters = kclusters, random_state=0).fit(RestaurantClustering)
kmeans.labels_[0:10]

array([3, 6, 1, 1, 3, 6, 1, 3, 3, 3], dtype=int32)

# add clustering labels
neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_) #This only needs to be run once?

#manhattan_merged = manhattan_data
restaurant_merged = AllRestaurants

# merge manhattan_grouped with manhattan_data to add latitude/longitude for each neighborhood
#manhattan_merged = manhattan_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')
restaurant_merged = restaurant_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on = 'Neighborhood')

#manhattan_merged.head() # check the last columns!
restaurant_merged.drop(['Neighborhood_Latitude','Neighborhood_Longitude','Venue_Category','Venue'], axis = 1, inplace = True)
#restaurant_merged.head()
#restaurant_merged.shape

#neighborhoods_venues_sorted
#restaurant_merged.head(15)
restaurant_merged.shape

(903, 14)

Visualization of the Neigborhood Clusters¶

# create map
map_clusters = folium.Map(location=[latitude, longitude], zoom_start=12)

# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

# add markers to the map
markers_colors = []
for lat, lon, poi, cluster in zip(restaurant_merged['Latitude'], restaurant_merged['Longitude'], restaurant_merged['Neighborhood'], restaurant_merged['Cluster Labels']):
    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=2,
        popup=label,
        color=rainbow[cluster-1],
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.7).add_to(map_clusters)
       
map_clusters

Further Analysis using Visual Tools¶

#Datasets that can be used for Analysis

#neighborhoods_venues_sorted.head(15) #This table shows the frequency of restaurants in neighborhoods #Note where Mexican Restaurants occur
#AllRestaurants.tail(15) #This table show coordinates for All Restaurants (including Mexican) pulled from FourSquare for Manhattan
#mexicanRestaurants.head()
pd_Restaurant = restaurant_onehot.groupby('Neighborhood').sum()
pd_Restaurant.columns = [column.replace(" ","_") for column in pd_Restaurant]
NoMexicanRestaurant = pd_Restaurant[pd_Restaurant.Mexican_Restaurant == 0]
NoMexicanRestaurant #These neighborhoods have NO MEXICAN RESTAURANTS
#pd_Restaurant.head()

import matplotlib.pyplot as plt

	Borough	Neighborhood	Latitude	Longitude
0	Bronx	Wakefield	40.894705	-73.847201
1	Bronx	Co-op City	40.874294	-73.829939
2	Bronx	Eastchester	40.887556	-73.827806
3	Bronx	Fieldston	40.895437	-73.905643
4	Bronx	Riverdale	40.890834	-73.912585

	Neighborhood	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
3204	Hudson Yards	40.756658	-74.000111	Spanish Diner	40.752394	-74.001491	Spanish Restaurant
3205	Hudson Yards	40.756658	-74.000111	Romeo and Juliet Coffee	40.760726	-73.997724	Coffee Shop
3206	Hudson Yards	40.756658	-74.000111	Il Punto Ristorante	40.756079	-73.994594	Italian Restaurant
3207	Hudson Yards	40.756658	-74.000111	Rosewood Theatre	40.757999	-73.999582	Nightclub
3208	Hudson Yards	40.756658	-74.000111	Thai Select	40.754867	-73.995007	Thai Restaurant
3209	Hudson Yards	40.756658	-74.000111	Silver Towers Dog Run	40.760854	-73.999765	Dog Run
3210	Hudson Yards	40.756658	-74.000111	Uncle Jack's Steakhouse	40.753619	-73.996019	Steakhouse
3211	Hudson Yards	40.756658	-74.000111	Treadwell	40.759964	-73.996284	Restaurant
3212	Hudson Yards	40.756658	-74.000111	Playboy Club New York	40.760000	-73.996367	Lounge
3213	Hudson Yards	40.756658	-74.000111	Cachet Boutique Hotel	40.759773	-73.996460	Hotel
3214	Hudson Yards	40.756658	-74.000111	StarDust	40.759869	-73.996460	Nightclub
3215	Hudson Yards	40.756658	-74.000111	Big George's Smokehouse	40.757954	-74.002296	BBQ Joint
3216	Hudson Yards	40.756658	-74.000111	NY Waterway 42nd St Bus	40.760050	-74.003379	Bus Station
3217	Hudson Yards	40.756658	-74.000111	Twilight Cruise By Citysightseeing	40.759744	-74.004096	Boat or Ferry
3218	Hudson Yards	40.756658	-74.000111	City Lights Cruises	40.759804	-74.004025	Boat or Ferry

	Neighborhood	Neighborhood_Latitude	Neighborhood_Longitude	Venue	Latitude	Longitude	Venue_Category
84	Chinatown	40.715618	-73.994279	Factory Tamal	40.715876	-73.990467	Mexican Restaurant
94	Chinatown	40.715618	-73.994279	JaJaJa Plantas Mexicana	40.714198	-73.990157	Mexican Restaurant
172	Washington Heights	40.851903	-73.936900	Refried Beans Mexican Restaurant	40.855039	-73.937031	Mexican Restaurant
214	Inwood	40.867684	-73.921210	Guadalupe Bar and Grill	40.867334	-73.920863	Mexican Restaurant
235	Inwood	40.867684	-73.921210	Guacamole	40.869659	-73.916736	Mexican Restaurant

	Neighborhood	Chinese Restaurant	Greek Restaurant	Seafood Restaurant
12	Marble Hill	0	0	1
26	Chinatown	0	1	0
27	Chinatown	1	0	0
32	Chinatown	1	0	0
36	Chinatown	1	0	0

	Neighborhood	African Restaurant	American Restaurant	Argentinian Restaurant	Asian Restaurant	Austrian Restaurant	Cantonese Restaurant	Caribbean Restaurant	Chinese Restaurant	Dim Sum Restaurant	Dumpling Restaurant	Ethiopian Restaurant	Fast Food Restaurant	French Restaurant	Greek Restaurant	Hotpot Restaurant	Indian Restaurant	Israeli Restaurant	Italian Restaurant	Japanese Restaurant	Kosher Restaurant	Malay Restaurant	Mediterranean Restaurant	Mexican Restaurant	Middle Eastern Restaurant	New American Restaurant	Paella Restaurant	Ramen Restaurant	Restaurant	Seafood Restaurant	Shanghai Restaurant	Southern / Soul Food Restaurant	Spanish Restaurant	Sushi Restaurant	Taiwanese Restaurant	Tapas Restaurant	Thai Restaurant	Vegetarian / Vegan Restaurant	Vietnamese Restaurant
0	Battery Park City	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.250000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1	Carnegie Hill	0.0	0.000000	0.045455	0.000000	0.000000	0.000000	0.000000	0.045455	0.000000	0.000000	0.000000	0.045455	0.136364	0.000000	0.000000	0.090909	0.000000	0.136364	0.045455	0.045455	0.000000	0.045455	0.045455	0.000000	0.045455	0.000000	0.045455	0.045455	0.000000	0.000000	0.000000	0.000000	0.045455	0.000000	0.000000	0.000000	0.045455	0.090909
2	Central Harlem	0.2	0.133333	0.000000	0.000000	0.000000	0.000000	0.066667	0.133333	0.000000	0.000000	0.066667	0.000000	0.133333	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.133333	0.000000	0.066667	0.000000	0.000000	0.000000	0.066667	0.000000	0.000000	0.000000
3	Chelsea	0.0	0.181818	0.000000	0.045455	0.000000	0.000000	0.000000	0.045455	0.000000	0.000000	0.000000	0.000000	0.090909	0.000000	0.000000	0.045455	0.045455	0.136364	0.090909	0.000000	0.000000	0.000000	0.000000	0.045455	0.045455	0.045455	0.000000	0.045455	0.045455	0.000000	0.000000	0.000000	0.045455	0.000000	0.045455	0.000000	0.000000	0.000000
4	Chinatown	0.0	0.105263	0.000000	0.052632	0.026316	0.026316	0.000000	0.236842	0.052632	0.026316	0.000000	0.000000	0.000000	0.052632	0.105263	0.000000	0.000000	0.026316	0.000000	0.000000	0.052632	0.000000	0.052632	0.000000	0.026316	0.000000	0.000000	0.000000	0.000000	0.026316	0.000000	0.026316	0.000000	0.026316	0.000000	0.026316	0.026316	0.026316

	Borough	Neighborhood	Latitude	Longitude
0	Manhattan	Marble Hill	40.876551	-73.910660
1	Manhattan	Chinatown	40.715618	-73.994279
2	Manhattan	Washington Heights	40.851903	-73.936900
3	Manhattan	Inwood	40.867684	-73.921210
4	Manhattan	Hamilton Heights	40.823604	-73.949688

	African_Restaurant	American_Restaurant	Arepa_Restaurant	Argentinian_Restaurant	Asian_Restaurant	Australian_Restaurant	Austrian_Restaurant	Brazilian_Restaurant	Cajun_/_Creole_Restaurant	Cantonese_Restaurant	Caribbean_Restaurant	Chinese_Restaurant	Comfort_Food_Restaurant	Cuban_Restaurant	Czech_Restaurant	Dim_Sum_Restaurant	Dumpling_Restaurant	Eastern_European_Restaurant	Empanada_Restaurant	Ethiopian_Restaurant	Falafel_Restaurant	Fast_Food_Restaurant	Filipino_Restaurant	French_Restaurant	German_Restaurant	Greek_Restaurant	Hawaiian_Restaurant	Himalayan_Restaurant	Hotpot_Restaurant	Indian_Restaurant	Israeli_Restaurant	Italian_Restaurant	Japanese_Curry_Restaurant	Japanese_Restaurant	Jewish_Restaurant	Kebab_Restaurant	Korean_Restaurant	Kosher_Restaurant	Latin_American_Restaurant	Lebanese_Restaurant	Malay_Restaurant	Mediterranean_Restaurant	Mexican_Restaurant	Middle_Eastern_Restaurant	Modern_European_Restaurant	Molecular_Gastronomy_Restaurant	Moroccan_Restaurant	New_American_Restaurant	North_Indian_Restaurant	Paella_Restaurant	Persian_Restaurant	Peruvian_Restaurant	Ramen_Restaurant	Restaurant	Russian_Restaurant	Scandinavian_Restaurant	Seafood_Restaurant	Shanghai_Restaurant	Soba_Restaurant	South_American_Restaurant	South_Indian_Restaurant	Southern_/_Soul_Food_Restaurant	Spanish_Restaurant	Sushi_Restaurant	Swiss_Restaurant	Szechuan_Restaurant	Taiwanese_Restaurant	Tapas_Restaurant	Thai_Restaurant	Theme_Restaurant	Turkish_Restaurant	Udon_Restaurant	Vegetarian_/_Vegan_Restaurant	Venezuelan_Restaurant	Vietnamese_Restaurant
Neighborhood
Central Harlem	3	2	0	0	0	0	0	0	0	0	1	2	0	0	0	0	0	0	0	1	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0
Chelsea	0	4	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	1	1	3	0	2	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0
Civic Center	0	3	0	0	1	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	4	0	0	0	0	0	1	0	3	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	1	0	0
Clinton	0	5	0	0	0	0	0	1	0	0	0	2	0	0	0	1	0	0	0	1	0	0	0	2	0	0	0	0	0	0	0	5	0	0	0	1	1	0	0	0	0	2	0	0	0	0	0	1	0	0	0	1	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0
Greenwich Village	0	2	0	0	0	0	0	0	0	0	2	3	0	1	0	0	0	1	0	0	1	0	0	2	0	0	0	0	0	3	0	11	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	1	0	0	0	0	1	1	0	0	2	0	0	0	0	0	0	4	0	0	0	1	1	1	0	1	1	0	2
Hudson Yards	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	2	0	0	0	0	0	0
Marble Hill	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Midtown	0	1	0	0	0	0	0	0	0	0	0	1	0	2	0	0	0	0	0	0	0	1	0	1	0	0	1	0	0	1	0	1	0	1	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	3	0	1	0	0	0	0	0	0	0	0	1
Midtown South	0	3	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	4	0	0	17	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
Roosevelt Island	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Sutton Place	0	2	0	0	1	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	2	0	6	0	0	0	0	0	0	2	1	0	2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	2	0	0	0	2	0	1
Turtle Bay	0	2	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	1	2	0	0	0	1	0	5	1	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	3	1	0	0	3	0	1	0	0	0	0	4	0	0	0	0	2	0	2	0	0	0	0
West Village	0	6	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	2	0	0	0	0	0	1	0	9	0	0	0	0	1	0	1	0	0	1	0	0	0	0	0	5	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0